A most welcomed convenience in this case is that fair prediction relies on general purpose ML algorithms that can be chosen from a pool arguably much larger than if it were restricted to ML algorithms specifically designed to be fair. Based on the example of a popular notion of fairness in prediction known as equal opportunity [1]}, we show how such an implication does indeed hold from representation rate fairness. We let \(Y\)  denote a target class, \(\hat{Y}\)  the predicted class, and \(A\)  is still our sensitive attribute. For the sake of simplicity, all those attributes have binary domains, e.g., \(Y\)  is an admission to college variable, \(A\)  is a race-related attribute, etc. With \(\rho >0\)  being fixed, we say that \(\hat{Y}\)  satisfies \(\rho \) -equal opportunity with respect to \(Y, A\)  if
\(\frac{p[\hat{Y} = 1 | A = a_i, Y=1]}{p[\hat{Y} = 1 | A = a_j, Y=1]} & \ge & \rho , \forall a_i, a_j \in \mathcal {A}.\) 
